StatSynth AI

About

StatSynth AI is a statistical synthetic data generation engine built around constraint-based data simulation rather than simple random data generation.The core technology can generate structured synthetic datasets based on user-defined sample sizes, variable types, ranges, distributions, categorical proportions, correlations, regression relationships, group differences, reliability-related requirements, and other measurable statistical constraints.For common workloads, datasets can be generated and validated within seconds with very low marginal compute cost.Current capabilities include:Custom sample sizes and variable structuresContinuous, categorical, and Likert-scale dataCustom value ranges and distributionsCorrelation constraintsRegression relationshipsGroup differencesReliability and validity analysisKMO and Bartlett's testDescriptive statisticsPearson correlation analysisRegression analysisMulticollinearity testing including VIF / ToleranceXLSX, CSV, and SPSS-compatible outputsThe project also includes an AI-powered questionnaire generation module. Users can describe a research topic, variables, expected relationships, sample size, and analysis requirements, and the system can generate a questionnaire structure before producing corresponding synthetic data and statistical validation outputs.Commercial applicationsThe technology can be deployed as:A standalone synthetic data SaaSA paid data generation websiteAn API-based data generation serviceA software testing data platformA statistical simulation toolA machine learning test-data generatorA data analysis service backendA buyer can integrate user accounts, payment systems, subscriptions, order management, API billing, or automated file delivery to build a fully automated commercial service.What is includedFull project source codeCore synthetic data generation algorithmsAI questionnaire generation moduleExisting web/backend components included in the projectConfiguration and environment filesDeployment instructionsExample generated datasetsExample statistical analysis outputsThe primary value of the project is the statistical constraint generation engine, not the size of the codebase or the frontend design.Current limitation: SEM, CFA, and PLS-SEM specific model-fit constraints are not currently supported.All generated datasets are synthetic/simulated data and should not be represented as real collected observations.